AutoML Leaderboard
AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork
<< Go back
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
2.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.344652 |
nan |
| auc |
0.562013 |
nan |
| f1 |
0.952559 |
0.654741 |
| accuracy |
0.909416 |
0.654741 |
| precision |
0.942529 |
0.985706 |
| recall |
1 |
0.654741 |
| mcc |
0.10233 |
0.964525 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.344652 |
nan |
| auc |
0.562013 |
nan |
| f1 |
0.952559 |
0.654741 |
| accuracy |
0.909416 |
0.654741 |
| precision |
0.909416 |
0.654741 |
| recall |
1 |
0.654741 |
| mcc |
0 |
0.654741 |
Confusion matrix (at threshold=0.654741)
|
Predicted as C |
Predicted as N |
| Labeled as C |
0 |
76 |
| Labeled as N |
0 |
763 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 1_Baseline
<< Go back
Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.303888 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.952559 |
0.818084 |
| accuracy |
0.909416 |
0.818084 |
| precision |
0.909416 |
0.818084 |
| recall |
1 |
0.818084 |
| mcc |
0 |
0.818084 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.303888 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.952559 |
0.818084 |
| accuracy |
0.909416 |
0.818084 |
| precision |
0.909416 |
0.818084 |
| recall |
1 |
0.818084 |
| mcc |
0 |
0.818084 |
Confusion matrix (at threshold=0.818084)
|
Predicted as C |
Predicted as N |
| Labeled as C |
0 |
76 |
| Labeled as N |
0 |
763 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of Ensemble
<< Go back
Ensemble structure
| Model |
Weight |
| 4_Default_Xgboost |
1 |
Metric details
|
score |
threshold |
| logloss |
0.485151 |
nan |
| auc |
0.647255 |
nan |
| f1 |
0.952559 |
0.405666 |
| accuracy |
0.909416 |
0.405666 |
| precision |
0.951705 |
0.654602 |
| recall |
1 |
0.405666 |
| mcc |
0.182698 |
0.652818 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.485151 |
nan |
| auc |
0.647255 |
nan |
| f1 |
0.952559 |
0.405666 |
| accuracy |
0.909416 |
0.405666 |
| precision |
0.909416 |
0.405666 |
| recall |
1 |
0.405666 |
| mcc |
0 |
0.405666 |
Confusion matrix (at threshold=0.405666)
|
Predicted as C |
Predicted as N |
| Labeled as C |
0 |
76 |
| Labeled as N |
0 |
763 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 2_DecisionTree
<< Go back
Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
6.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.306519 |
nan |
| auc |
0.529015 |
nan |
| f1 |
0.952559 |
0.465517 |
| accuracy |
0.909416 |
0.465517 |
| precision |
0.915254 |
0.863464 |
| recall |
1 |
0.465517 |
| mcc |
0.139959 |
0.710468 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.306519 |
nan |
| auc |
0.529015 |
nan |
| f1 |
0.952559 |
0.465517 |
| accuracy |
0.909416 |
0.465517 |
| precision |
0.909416 |
0.465517 |
| recall |
1 |
0.465517 |
| mcc |
0 |
0.465517 |
Confusion matrix (at threshold=0.465517)
|
Predicted as C |
Predicted as N |
| Labeled as C |
0 |
76 |
| Labeled as N |
0 |
763 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 6_Default_RandomForest
<< Go back
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
11.8 seconds
Metric details
|
score |
threshold |
| logloss |
0.298998 |
nan |
| auc |
0.595046 |
nan |
| f1 |
0.952559 |
0.500223 |
| accuracy |
0.909416 |
0.500223 |
| precision |
0.958333 |
0.949446 |
| recall |
1 |
0.500223 |
| mcc |
0.12452 |
0.883034 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.298998 |
nan |
| auc |
0.595046 |
nan |
| f1 |
0.952559 |
0.500223 |
| accuracy |
0.909416 |
0.500223 |
| precision |
0.909416 |
0.500223 |
| recall |
1 |
0.500223 |
| mcc |
0 |
0.500223 |
Confusion matrix (at threshold=0.500223)
|
Predicted as C |
Predicted as N |
| Labeled as C |
0 |
76 |
| Labeled as N |
0 |
763 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 4_Default_Xgboost
<< Go back
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
51.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.485151 |
nan |
| auc |
0.647255 |
nan |
| f1 |
0.952559 |
0.405666 |
| accuracy |
0.909416 |
0.405666 |
| precision |
0.951705 |
0.654602 |
| recall |
1 |
0.405666 |
| mcc |
0.182698 |
0.652818 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.485151 |
nan |
| auc |
0.647255 |
nan |
| f1 |
0.952559 |
0.405666 |
| accuracy |
0.909416 |
0.405666 |
| precision |
0.909416 |
0.405666 |
| recall |
1 |
0.405666 |
| mcc |
0 |
0.405666 |
Confusion matrix (at threshold=0.405666)
|
Predicted as C |
Predicted as N |
| Labeled as C |
0 |
76 |
| Labeled as N |
0 |
763 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 3_Linear
<< Go back
Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
4.8 seconds
Metric details
|
score |
threshold |
| logloss |
0.313337 |
nan |
| auc |
0.584224 |
nan |
| f1 |
0.952559 |
0.14372 |
| accuracy |
0.909416 |
0.14372 |
| precision |
0.965812 |
0.967649 |
| recall |
1 |
0.14372 |
| mcc |
0.120534 |
0.813619 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.313337 |
nan |
| auc |
0.584224 |
nan |
| f1 |
0.952559 |
0.14372 |
| accuracy |
0.909416 |
0.14372 |
| precision |
0.909416 |
0.14372 |
| recall |
1 |
0.14372 |
| mcc |
0 |
0.14372 |
Confusion matrix (at threshold=0.14372)
|
Predicted as C |
Predicted as N |
| Labeled as C |
0 |
76 |
| Labeled as N |
0 |
763 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back